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基于SCADA/PMU混合量测的电力系统动态状态估计算法,目前主要是先进行量测变换再进行动态估计,尝试结合静态估计和动态估计的优点,提出一种新的方法。该方法首先对SCADA量测数据运用经典的加权最小二乘法(WLS)进行静态状态估计,估计结果和PMU量测中的电压相量共同形成量测数据集,再进行线性动态状态估计。WLS方法能保证状态估计的质量和算法的收敛性能,同时线性动态估计只选用PMU量测的电压相量,避免量测变换及其产生的误差,而且雅可比矩阵高度稀疏,计算时间较短。在IEEE14节点系统上的仿真结果表明,此方法具有较好的滤波和预测效果。
Based on hybrid measurement of SCADA / PMU, the dynamic state estimation algorithm for power system is mainly based on the first measurement and then the dynamic estimation. In this paper, a new method is proposed by combining the advantages of static estimation and dynamic estimation. In this method, the static state estimation is made by using the classical weighted least square method (WLS) for the SCADA measurement data. The estimation result and the voltage phasor in the PMU measurement together form a measurement dataset, and then the linear dynamic state estimation is performed. The WLS method can guarantee the quality of the state estimation and the convergence performance of the algorithm. At the same time, only the voltage phasor measured by the PMU is used in the linear dynamic estimation, which avoids the error of the measurement transformation and its measurement. Moreover, the Jacobian matrix is highly sparse and the computation time is short. The simulation results on the IEEE14 node system show that this method has better filtering and prediction effects.